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Machine Learning Engineer Internship, Quantization - EMEA Remote

At Hugging Face, we’re on a journey to democratize good AI. We are building the fastest growing platform for AI builders with over 5 million users & 100k organizations who collectively shared over 1M models, 300k datasets & 300k apps. Our open-source libraries have more than 400k+ stars on Github.

About the Role

Quantization is a technique to reduce the computational and memory costs of running inference by representing the weights and activations with low-precision data types like 8-bit integer (int8) instead of the usual 32-bit floating point (float32). It is a very promising technique as it allows to run and fine-tune on consumer-grade hardware LLMs with minimal performance degradation.

This internship works at the intersections of software engineering, machine learning engineering, and education. The focus will be to integrate new quantization methods in Hugging Face ecosystem (transformers, accelerate, peft, diffusers), maintain existing integration (bitsandbytes, awq, autogptq) as well as making sure that the community is aware of these tools through benchmarks and blogposts. The ultimate goal of this internship is to drive forward quantization in the open source ecosystem.

About You

If you love open-source but also have an eye for art and creativity, are passionate about making complex technology more accessible to engineers and artists, and want to contribute to one of the fastest-growing ML ecosystems, then we can't wait to see your application!

If you're interested in joining us, but don't tick every box above, we still encourage you to apply! We're building a diverse team whose skills, experiences, and background complement one another. We're happy to consider where you might be able to make the biggest impact.

More about Hugging Face

We are actively working to build a culture that values diversity, equity, and inclusivity. We are intentionally building a workplace where people feel respected and supported—regardless of who you are or where you come from. We believe this is foundational to building a great company and community. Hugging Face is an equal opportunity employer and we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

We value development. You will work with some of the smartest people in our industry. We are an organization that has a bias for impact and is always challenging ourselves to continuously grow. We provide all employees with reimbursement for relevant conferences, training, and education.

We care about your well-being. We offer flexible working hours and remote options. We support our employees wherever they are. While we have office spaces around the world, especially in the US, Canada, and Europe, we're very distributed and all remote employees have the opportunity to visit our offices. If needed, we'll also outfit your workstation to ensure you succeed.

We support the community. We believe significant scientific advancements are the result of collaboration across the field. Join a community supporting the ML/AI community.

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What You Should Know About Machine Learning Engineer Internship, Quantization - EMEA Remote, Hugging Face

Are you ready to dive into the exciting world of artificial intelligence and machine learning? Join Hugging Face as a Machine Learning Engineer Intern specializing in Quantization! At Hugging Face, we’re on a mission to democratize great AI, powering a community of over 5 million users and 100k organizations. With over 1 million models and a thriving open-source community, we’re building one of the fastest-growing platforms for AI builders. This internship will put you at the crossroads of software engineering, machine learning, and education as you help to integrate innovative quantization methods into our ecosystem. We want you to explore how low-precision data types can make a significant impact in running large language models with minimal performance degradation. Beyond coding, you’ll engage with our community, creating benchmarks, blog posts, and all the resources needed to keep everyone in the loop about quantization tools. We treasure diversity and creativity, so if you’re passionate about making complex tech accessible and have the urge to bring your artistic flair to the tech scene, we’d love to see your application! Our culture values respect and growth, and you'll be surrounded by some of the brightest minds in the industry while enjoying flexible hours and a remote work-friendly environment. So, if you’re excited to contribute to the future of Machine Learning at Hugging Face, this is your chance!

Frequently Asked Questions (FAQs) for Machine Learning Engineer Internship, Quantization - EMEA Remote Role at Hugging Face
What does a Machine Learning Engineer Internship at Hugging Face involve?

As a Machine Learning Engineer Intern at Hugging Face, you will work on integrating new quantization methods into our ecosystem. This involves collaborating with software engineering and machine learning teams, maintaining existing tools, and creating educational content such as benchmarks and blog posts. Your role is crucial in advancing quantization techniques in the open-source community.

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What skills are required for the Machine Learning Engineer Internship at Hugging Face?

Ideal candidates for the Machine Learning Engineer Internship at Hugging Face should be passionate about open-source projects and have knowledge of machine learning principles. Familiarity with software engineering practices is essential, along with a creative mindset aimed at simplifying complex technologies for a broader audience.

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What is the focus of quantization in ML as a Machine Learning Engineer Intern at Hugging Face?

In your role as a Machine Learning Engineer Intern at Hugging Face, you will focus on quantization as a method to enhance efficiency by using lower precision data types. You’ll learn how to apply quantization techniques to large models while ensuring performance remains high, making cutting-edge technology accessible even on consumer-grade hardware.

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How can I contact Hugging Face about the Machine Learning Engineer Internship?

For any inquiries concerning the Machine Learning Engineer Internship at Hugging Face, you can typically find contact information on their official website or within the job posting. You may want to reach out through their contact forms or direct email options provided.

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What opportunities for growth exist as a Machine Learning Engineer Intern at Hugging Face?

Hugging Face fosters a collaborative environment where as a Machine Learning Engineer Intern, you'll have access to mentorship from industry experts. The focus is on your professional development, with opportunities for conferences, workshops, and access to continuous education in the latest ML technologies.

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What is the company culture like at Hugging Face for interns?

Hugging Face is committed to creating a culture of diversity, equity, and inclusion. As an intern, you will find an environment that respects and supports every individual. The company values creativity and encourages feedback, ensuring that you feel valued during your time with the team.

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What remote work options are available for the Machine Learning Engineer Internship at Hugging Face?

Hugging Face recognizes the benefits of flexible work arrangements, offering remote work options for their Machine Learning Engineer Internship. You can work from anywhere and still collaborate with teams worldwide, with opportunities to visit office spaces if desired.

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Common Interview Questions for Machine Learning Engineer Internship, Quantization - EMEA Remote
What is your understanding of quantization in machine learning?

To respond effectively, explain quantization as a method to reduce model size and improve inference times by using lower precision data types. Give examples of how you would apply quantization techniques in real-world scenarios, highlighting its benefits in terms of performance and efficiency.

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Can you explain the significance of using low-precision in AI models?

Illustrate that using low-precision, like int8 instead of float32, can significantly decrease the computational load required for running models, allowing them to function efficiently on less powerful hardware. Discuss scenarios where this is particularly advantageous, such as mobile applications or edge computing.

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How would you approach integrating new quantization methods into existing frameworks?

Share a structured approach, starting with understanding the current frameworks in place. Talk about how you would analyze the strengths and weaknesses of existing methods, design new integration strategies, and collaborate with team members to ensure smooth transitions.

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What tools or frameworks are you familiar with that relate to machine learning and quantization?

Mention popular tools such as Hugging Face's Transformers, TensorFlow, PyTorch, and libraries specifically for quantization like BitsAndBytes or AutoGPTQ. Discuss your experiences using these tools, emphasizing their applications within your projects.

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Tell us about a project where you applied machine learning techniques.

Prepare a detailed response that includes the project’s goal, the methods you used, the challenges you faced, and the results. Highlight collaboration, learning moments, and how this experience has prepared you for the internship at Hugging Face.

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How do you stay updated with the latest trends in machine learning and AI?

Respond by sharing specific resources you utilize, such as publications, AI research journals, online communities, or conferences. Emphasize your proactive approach to continuous learning in the rapidly evolving field of machine learning to showcase your dedication.

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What do you think makes a good Machine Learning Engineer?

Discuss key attributes such as analytical skills, creativity, and problem-solving. Mention the importance of collaboration and effective communication in a team-oriented environment, as these qualities significantly influence success in a role like the internship at Hugging Face.

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Describe your understanding of the Hugging Face ecosystem.

Express familiarity with the Hugging Face community, its platform for sharing models, datasets, and tools. Discuss key projects like Transformers and how they contribute to democratizing AI, and mention a couple of their initiatives that resonate with you.

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How would you explain a complex technical concept to a non-technical audience?

Illustrate your communication skills by providing a structured explanation of the concept in simple terms, using analogies or relatable examples. Emphasize the importance of making technology accessible while fostering understanding within broader audiences.

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What are your thoughts on diversity in tech, especially at companies like Hugging Face?

Share your viewpoints on how diversity encourages innovation and creativity within tech teams. Reflect on Hugging Face’s commitment to inclusiveness and your belief that diverse perspectives lead to improved problem-solving and product development.

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Internship, remote
DATE POSTED
November 28, 2024

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